code: def __init__(
      self,
      input_size: int,
      max_sequence_length: int,
      hidden_size: int,
      num_heads: int,
      output_size: int,
      dropout: float = 0.1,
  ):
      super().__init__()
      self.input_size = input_size
      self.max_sequence_length = max_sequence_length
      self.hidden_size = hidden_size
  
      self.self_attention = MultiHeadSelfAttention(
          input_size, hidden_size, num_heads=num_heads
      )
      self.dropout1 = nn.Dropout(dropout)
      self.layernorm1 = nn.LayerNorm(hidden_size, eps=1e-6)
      self.fully_connected = nn.Sequential(
          nn.Linear(input_size, output_size),
          get_activation("relu"),
          nn.Linear(output_size, hidden_size),
      )
      self.dropout2 = nn.Dropout(dropout)
      self.layernorm2 = nn.LayerNorm(hidden_size, eps=1e-6)
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from ludwig.utils.torch_utils import get_activation, LudwigModule
label_desc: Use image node_id 1 to create a new TransformerBlock object from inherited base classes: LudwigModule with example: obj = TransformerBlock(input_size, max_sequence_length, hidden_size, num_heads, output_size, dropout)